Long-distance dependencies are notoriously difficult to analyze in a formally explicit way because they involve constituents that seem to have been extracted from their canonical position in an utterance. The most widespread solution is to identify a gap at an extraction site and to communicate information about that gap to its filler, as in What_FILLERdid you see_GAP? This paper rejects the filler−gap solution and proposes a cognitive-functional alternative in which long-distance dependencies spontaneously emerge as a side effect of how grammatical constructions interact with each other for expressing different conceptualizations. The proposal is supported by a computational implementation in Fluid Construction Grammar that works for both parsing and production.

While this paper was undergoing review, I learned the sad news of Ivan Sag’s passing away. His contributions to the field can hardly be overestimated, and it is with the utmost respect for his work that I disagree with his analysis of long-distance dependencies. The research reported in this paper has been conducted at and funded by the Sony Computer Science Laboratory Paris. I would like to thank Luc Steels, director of Sony CSL Paris, for his feedback and support. I also thank Pieter Wellens from the VUB AI-Lab for his recent additions to FCG that have made this implementation possible. I also thank Frank Richter (University of Tübingen) and Stefan Müller (Free University of Berlin) for helping me to better understand HPSG. Finally, I would like to thank the editors and reviewers of Language and Cognition for their efforts that have helped to improve this paper. All remaining errors are of course my own.

Footnotes

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While this paper was undergoing review, I learned the sad news of Ivan Sag’s passing away. His contributions to the field can hardly be overestimated, and it is with the utmost respect for his work that I disagree with his analysis of long-distance dependencies. The research reported in this paper has been conducted at and funded by the Sony Computer Science Laboratory Paris. I would like to thank Luc Steels, director of Sony CSL Paris, for his feedback and support. I also thank Pieter Wellens from the VUB AI-Lab for his recent additions to FCG that have made this implementation possible. I also thank Frank Richter (University of Tübingen) and Stefan Müller (Free University of Berlin) for helping me to better understand HPSG. Finally, I would like to thank the editors and reviewers of Language and Cognition for their efforts that have helped to improve this paper. All remaining errors are of course my own.